Modulation recognition method based on convolutional neural network and cyclic spectrum images

Abstract:

An intelligent modulation recognition method based on the Convolutional Neural Network(CNN) and two-dimensional Red-Green-Blue(RGB) cyclic spectrum images is proposed in order to improve the modulation recognition accuracy and reduce the computational complexity. The cyclic spectrum can be employed to identify the modulation type. The three-dimensional cyclic spectra are converted to two-dimensional RGB cyclic spectra to reduce the computational complexity, which are then taken to build the data set. Moreover, a CNN based modulation classifier with low computational complexity is proposed. Simulation results show that the proposed intelligent modulation recognition algorithm can achieve higher classification accuracy with lower computational complexity.